How to make HR Analytics Simple and Human - Analytics in HR

How to make HR Analytics Simple and Human

A lot of HR professionals ask the question “what is HR analytics?” and want to make analytics part of their profession. This post covers the basics...

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A lot of HR professionals ask the question “what is HR analytics?” and want to make analytics part of their profession. This post covers the basics of HR analytics and a few ways to simplify its implementation. Let’s start with the big question.


What is HR analytics?

An in-depth analysis of an organization’s people problems by the human resource department is called HR analytics.

According to the scientific definition of HR analytics, “it is the systematic identification and quantification of the people drivers of business outcomes”.

Consider the following questions, and check if you can answer them.

  • What is the rate of your annual employee turnover?
  • Does your employee turnover consist of regretted loss? If yes, then by how much?
  • What is the total number of employees who are most likely to leave your company within a year?

The use of HR data makes it possible for you to answer all these questions correctly. Though many HR professionals can answer the first question quickly; it is harder to explain the second one.

You would need to combine two different data sources to answer the second question. However, to answer the third one, you would need some serious data-science experience.

HR departments have been collecting data for a long time, but this data has not been analyzed comprehensively. When organizations start realizing that they have people problems, they switch to HR analytics to solve them. Giving impetus to correct processes in Data Wrangling can aid the HR Department in mining good data which can be put to use.


Why is HR analytics important in organizations?

Most HR decisions are based on gut feelings and professional instincts. The results of recruitment often rely on the personal connection that recruiters make with a candidate during an interview. These decisions made on the basis of gut feelings are often incorrect and can even normalize bad practices.

HR analytics can predict models and help boost performance to ensure success in decision-making. It eliminates potential human errors that lead to greater accuracy in overall decisions. For instance, data analytics can significantly improve workload management as team leaders are able to understand which teams are bearing additional burden and which teams are overstaffed.

According to a whitepaper on HR analytics by Oracle, a higher level of analytics is associated with:

  • Hike sales growth by 8%
  • Generate higher net operating income by 24%
  • Increase sales per employee by 58%.

If your organization is new to HR analytics and looks for simple ways to implement it, then the steps mentioned below can help set the process.

  1. Define the business questions

First, you need to define the business questions that you want to solve. This way you can lay systematic foundation in HR analytics. It is quite messy to start gathering data at random and then blindly try to find correlations.

All HR-related issues should be defined. These issues are related to topics like better employee retention rates, workplace diversity, calculating the total amount of money spent on training sessions, and an understanding of workplace absence levels. It is easier to start with simple issues and focus later on the more complex problems.

As soon as you get completely aware of all issues related to HR, you can start outlining the required metrics to scrutinize them.

The key HR metrics that reveal an HR department’s performance and improvement are:

The resignation rate – What is the total number of employees resigning within a specific period?

The recruitment time – What is the total time lag that occurs between the steps of closing a vacant job position to the absorption of an employee into the workforce.

Staff turnover rate – How many recruits leave after a year, five years and so on?

Workforce diversity – What are the percentages of women, men, religious groups, and ethnicities at the workplace?

The revenue from employees – How much revenue does a company generate per full-time worker?

Pay for overtime– How high is an overtime pay and is it implemented regularly?


  1. Discover the data that answers all the questions

You can start identifying the data required to answer and solve them once all the questions and the problems are defined.

First, your focus should be on HR-related data that your department has stored already. The data should contain all information regarding recruitment, performance, and succession. These common datasets may already be monitored by HR department.

Second, most of the companies gather data on employee engagement, surveys and exit interviews. If your company has a higher level of data gathering; the created datasets are likely to be there.

Finally, make sure that you extend data gathering to other departments and business systems. Finance and market research are two critical departments that you should focus to gather data. These involve things: sales performance, total money spent on training and market research, and employee turnover.

  1. Implementation of ETL

It is essential that the HR department works closely with the IT department because software and data extraction require specialized data analytic skills. Therefore, it is a good idea to develop connections between the two departments. Getting people skilled in Data Science is definitely going to add value. Here’s a list published on IBM about the top Data science courses that might be of help. One of my favorites is a data science course that focusses on R – which most analytics professionals are familiar with.

The implementation of ETL is a part of this process, and it stands for extraction, transformation, and loading. You can automatically implement this process with the help of tools like Apache Hadoop, Microsoft SQL Server Integration Services, and IBM’s WebSphere DataStage are the top options. I especially like Hadoop, as it has become almost simultaneously synonymous with analysis for me.

You will be able to extract necessary data from your defined source with this process, organize into a consistent and clean format, and use it in analysis after loading it to your analytical platform.

  1. Integrate the findings to business operations

You need to start implementing changes after your HR data analytics start generating results. For instance, if workforce diversity is your focus and your data shows fewer applications from ethnic minorities then there is a need to reshape your recruiting strategy.

The strategy could include connecting recruitment agencies that target ethnic minority candidates, taking interviews within these groups to check the community views of your organization, and developing more tutoring opportunities with them.

Additionally, it is essential to draw a connection between other business measures and HR data. For example, relaxing staff overtime directly correlates to profitability and productivity at workplace.

According to a KPMG report, people are the real numbers, the value of this connection was explained by giving an example of workplace absence and cost-efficiency.

“Though it is possible to track absences by location or by looking at prior years if HR can show improvements in absenteeism correlate with manufacturing cost efficiency, then line leaders are likely to see the potential of HR,” the report affirmed.


Practice Regular analysis

Most times, irregular HR analytics will not show stable results. It is essential to implement a regular schedule to enjoy the benefits of the process.

Once you have defined an issue; you need to consider HR data to perform data analysis and to find an exact answer to the issue.

After the implementation of the solutions to your problem, you need to reevaluate the issue to check whether the changes are working or if new issues have arisen.

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